GEOG 336
Charlie Krueger
Creation of a Digital Elevation Surface-
Field Activity 4
INTRODUCTION:
In
the previous lab the class was asked to make a terrain that fit the outline of
the lab and contained changes in elevation that would represent things like a
ridge, depression, plain, valley, and hills. A wooden box was filled with sand
and from that each group would have to create a diverse terrain to then take
data from. Gathering the data from the terrain would be done by sampling the
area. Groups had to decide the type of sampling that would work best between
random, systematic, and stratified. The data would be collected and then placed
into an Excel spread sheet to then be used in this lab. The group decided to
sample with the method of systematic. A grid pattern was drawn up to make sure
that the spacing was even and then the pattern was placed over the sandbox
using string and push pins to secure the lines. Data collection then began and
was taken down by using a ruler and measuring at each one of the intersecting
points of the grid. Two group members measured as one was recording the data in
a field notebook.
Data
normalization is the process of reorganizing data so that it is in a database
were all the data that has been collected in stored together. This also helps
find mistakes in the data and possible overlaps. Making sure all the data is in
one place and easily accessible to group members and others. This was essential
for this lab because if the group did not normalize the data then only one of
the members would have had it. This would have only allowed one person to use
the data that the whole group was trying to use. This also let all the members
look at the data and see if any data points did not fit what the terrain was.
The
data points that were collected from the sampling only represents the values at
the points were the grid pattern overlaps. So the group had a total of 400 data
set points that would represent the terrain that was created. With
interpolation the data points could then represent the entire area of the
terrain and not just where the points were taken from. Interpolation estimates
the surface values of un sampled points of the terrain and gives them a value
based off of the surrounding points. Interpolation will help so that the final
map will represent the entire area and not just where points were collected at.
The map then will look like the whole area was sampled even though only 400
points were.
METHODS:
When
creating the final product for this activity there were several choses of
interpolation that could have been used on the map. The first method of
interpolation is inverse distance weighted (IDW) and this method estimates cell
values by averaging the values of the data points that are around each cell.
Each once of the data points were the same distance from each other so the
program measured the same distance from each point to the center of a cell. So
with a grid pattern this method did not work out very well because it made the
map look bumpy and knobby even though the terrain was not like this. IDW would
be a good fit for areas that have dense data point set but would have trouble
with mountainous areas. The next interpolation that was an option was natural neighbor.
Natural neighbor finds the closest subset
of data and gives values to those points based on the area. It is basically stealing
data from other points to make a calculation to what the area is like between
those two points. This method was a viable option for creating the map and did
represent the terrain feature well. It also is designed to acknowledge minimum
and maximum values of data points which is a lot of what the data was at each
point. Natural neighbor can handle large
amounts of data points which would be very useful when having to survey a vast
area unlike the sandbox in our lab. Kriging was another interpolation method
that is offered on ArcMap. Kriging generates a surface from scatter points with
z-values that are attached to them. It uses an advanced geostatistical
procedure that estimates the surface from those z-values. The formula for this
method is very complex and this method is often used in soil science and
geology. It can though come into problems because the map does not pass through
the point values so this can cause values to look higher or lower than the real
ones. This was not the method that represented the data the best for the final
map. Another method that was not chosen to use on the map was Triangulated
Irregular Network (TIN) and it tries to create a surface in the form of
triangles connecting or surrounding the nearest points. TIN does not look
smooth in map form because of the triangles and really did not represent the
terrain well. The map looked jagged and strange where the area that was sampled
was smooth and rounded. The grid pattern also made the map look bizarre because
the points were perfectly spaced so the program had to create weird slopes and
edges on the map. The final method and the one that was chosen was spline
interpolation. Spline uses a method that estimates values using a math function
and minimizes curvature so that the map results in a smooth surface. The
surface that spline creates passes exactly through the data points and this was
a big advantage for this map creation. Spline is also good for surfaces that do
not change very quickly and the terrain in the area fit this. Although spline
worked in the lab it would not have been effective if the terrain had steep
cliffs because of the slope calculations. It would also be ineffective with
data points that close together and vary in value by a lot.
Spline
was the format that the 3D images was exported in and this allowed for a close
representation of the actual terrain that was sampled. The 3D image was created
in ArcScene and then was saved as a JPEG file and sent to Powerpoint. In
Powerpoint the (0,0) origin location could be added to the map and also an X, Y,
Z scale. The scale is just simple white lines with arrows that show how the map
is oriented. This allows any person who see the map to at least understand how
the map is set up.
RESULTS/DISCUSSION:
Looking
at the IDW map the lumpy edges stand out. This method would not have created a
map that looked anything like the terrain that was created. It did however
capture the features that were created on the terrain better that some of the
other methods. Looking at the figure the blue low points and high red points
are visible but are not esthetically pleasing for the eye. This method looked
even worse when placed into the 3D model.
 |
| IDW method |
The
TIN method was not close to becoming the map that was the final. The edges of
the features look absolutely nothing like those of the terrain and it comes off
blocky and strange looking. The plain is about the only area that was
represented on this map and that was a huge problem for this map. Also with
other maps a key is not really needed because a person could figure out the
representation but not on this map.
 |
| TIN Method |
The
Kriging is probably the worst of the method that was on ArcMap just because the
degree of the data that it was given. This map does not represent any of the
features well and really just looks like a painting of some type. This method
does not work well with the data that it was presented with from the group and
that is why the outlook is so unrepresentative of the terrain.
 |
| Kriging Method |
The
Natural Neighbor does a great job of representing the data. It was only not
picked because of the fact that spline could smooth out more of the feature and
that was what are terrain was. It was made of smooth sand and did not have any
large changes in terrain. Natural Neighbor just as spline does a nice job of
showing the depression in the top right corner and also the ridge that runs
along the right side along the Y axis. Spline was the option that was selected to
be the final map and it does a great job of showing the terrain that was
created. The best thing about spline is that it goes through each and every
point and this represents the data that we recorded. The image below was created in AcrMap then sent over to ArcScene where it was saved as a JPEG and moved over to Powerpoint to get the final touches.
 |
| Natural Neighbor Method (Note the resemblance to the Spline Method) |
 |
| Spline Method |
 |
| Final Map of the data taken from the sandbox |
SUMMARY/CONCLUSION:
This
survey relates to other field based surveys because the class went out and
conducted sampling of an area. Just like any other geographer would do in the situation
a plan was drawn up, put into action, and then the data that was collected was
used to create a map of the location. The biggest difference was that the area
was created by the groups and that it was very small in comparison to other
areas that get surveyed. Another issue is that the environment is always
changing where in the sand box it stayed the same until the sampling was
complete.
No it
is not realistic to perform such a detailed grid survey. Out in a large area
there is no way that a grid pattern would be used like it was in the class. It may
be used on a smaller section but on large areas it would take too much time and
be a hassle.
Yes,
it can be used for other methods besides elevation. In hydrology is can be used
to model how water will flow over certain terrain and just another example is a
wildlife management were people could use the functions when dealing with
wildlife point locations and the relationship to the environment.
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